Add features to the Dreambooth LoRA SDXL training script (#5508)

* Additions:
- support for different lr for text encoder
- support for Prodigy optimizer
- support for min snr gamma
- support for custom captions and dataset loading from the hub

* adjusted --caption_column behaviour (to -not- use the second column of the dataset by default if --caption_column is not provided)

* fixed --output_dir / --model_dir_name confusion

* added --repeats, --adam_weight_decay_text_encoder
+ some fixes

* Update examples/dreambooth/train_dreambooth_lora_sdxl.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update examples/dreambooth/train_dreambooth_lora_sdxl.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update examples/dreambooth/train_dreambooth_lora_sdxl.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* - import compute_snr from diffusers/training_utils.py
- cluster adamw together
- when using 'prodigy', if --train_text_...

examples/test_examples.py

@@ -421,6 +421,49 @@ class ExamplesTestsAccelerate(unittest.TestCase):
             )
             self.assertTrue(starts_with_unet)
 
+    def test_dreambooth_lora_sdxl_custom_captions(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            test_args = f"""
+                examples/dreambooth/train_dreambooth_lora_sdxl.py
+                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-xl-pipe
+                --dataset_name hf-internal-testing/dummy_image_text_data
+                --caption_column text
+                --instance_prompt photo
+                --resolution 64
+                --train_batch_size 1
+                --gradient_accumulation_steps 1
+                --max_train_steps 2
+                --learning_rate 5.0e-04
+                --scale_lr
+                --lr_scheduler constant
+                --lr_warmup_steps 0
+                --output_dir {tmpdir}
+                """.split()
+
+            run_command(self._launch_args + test_args)
+
+    def test_dreambooth_lora_sdxl_text_encoder_custom_captions(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            test_args = f"""
+                examples/dreambooth/train_dreambooth_lora_sdxl.py
+                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-xl-pipe
+                --dataset_name hf-internal-testing/dummy_image_text_data
+                --caption_column text
+                --instance_prompt photo
+                --resolution 64
+                --train_batch_size 1
+                --gradient_accumulation_steps 1
+                --max_train_steps 2
+                --learning_rate 5.0e-04
+                --scale_lr
+                --lr_scheduler constant
+                --lr_warmup_steps 0
+                --output_dir {tmpdir}
+                --train_text_encoder
+                """.split()
+
+            run_command(self._launch_args + test_args)
+
     def test_dreambooth_lora_sdxl_checkpointing_checkpoints_total_limit(self):
         pipeline_path = "hf-internal-testing/tiny-stable-diffusion-xl-pipe"